Discussions Regarding the Invalidity of States’ Consent in the Field of Public International Law

This article aims at a comparative analysis of the invalidity of states’ consent and examines the manner in which they are regulated in the Public International Law and in the domestic law. Thus, according to the dispositions of the Vienna Convention (1969) on the law of treaties between states, invalidity of states’ consent are: breach of the dispositions of the domestic law of the state regarding the competence to sign treaties; the error; the fraud; the corruption of a state representative; the coercion of a state representative; the coercion against a state. The invalidity of states’ consent are sanctioned both in the matter of the Public International Law as well as in the domestic civil law by nullity, either absolute (such as the coercion exercised on the state representative and the coercion against a state) or relative (such as the breach of the dispositions of the domestic law of the state regarding the competence to sign treaties, the error, the fraud or the corruption of a state representative). In certain cases, the dispositions of the domestic law are fully mirrored by the regulations of the Vienna Convention, as is the case of the fraud, but there are also cases – the breach of the dispositions of the domestic law of the state regarding the competence to sign treaties – where the Vienna Convention introduces a disposition which flagrantly contradicts the civil theory in the matter, the state being able to invoke itself the defect in its consent. At the same time, in the domestic civil law, the rule according to which no one can invoke his/her own turpitude is widely sanctioned. We conclude by showing that the manner of regulation of nullity taken over from the domestic civil law and transposed in the dispositions of the Vienna Convention fails to meet the exigencies and rigors of the definition of nullity.Public International Law, civil law, Vienna Convention

Comparison of methods for calculating conditional expectations of sufficient statistics for continuous time Markov chains

<p>Abstract</p> <p>Background</p> <p>Continuous time Markov chains (CTMCs) is a widely used model for describing the evolution of DNA sequences on the nucleotide, amino acid or codon level. The sufficient statistics for CTMCs are the time spent in a state and the number of changes between any two states. In applications past evolutionary events (exact times and types of changes) are unaccessible and the past must be inferred from DNA sequence data observed in the present.</p> <p>Results</p> <p>We describe and implement three algorithms for computing linear combinations of expected values of the sufficient statistics, conditioned on the end-points of the chain, and compare their performance with respect to accuracy and running time. The first algorithm is based on an eigenvalue decomposition of the rate matrix (EVD), the second on uniformization (UNI), and the third on integrals of matrix exponentials (EXPM). The implementation in R of the algorithms is available at <url>http://www.birc.au.dk/~paula/</url>.</p> <p>Conclusions</p> <p>We use two different models to analyze the accuracy and eight experiments to investigate the speed of the three algorithms. We find that they have similar accuracy and that EXPM is the slowest method. Furthermore we find that UNI is usually faster than EVD.</p

Inference of Distribution of Fitness Effects and Proportion of Adaptive Substitutions from Polymorphism Data

The distribution of fitness effects (DFE) encompasses the fraction of deleterious, neutral, and beneficial mutations. It conditions the evolutionary trajectory of populations, as well as the rate of adaptive molecular evolution (alpha). Inferring DFE and a from patterns of polymorphism, as given through the site frequency spectrum (SFS) and divergence data, has been a longstanding goal of evolutionary genetics. A widespread assumption shared by previous inference methods is that beneficial mutations only contribute negligibly to the polymorphism data. Hence, a DFE comprising only deleterious mutations tends to be estimated from SFS data, and alpha is then predicted by contrasting the SFS with divergence data from an outgroup. We develop a hierarchical probabilistic framework that extends previous methods to infer DFE and alpha from polymorphism data alone. We use extensive simulations to examine the performance of our method. While an outgroup is still needed to obtain an unfolded SFS, we show that both a DFE, comprising both deleterious and beneficial mutations, and alpha can be inferred without using divergence data. We also show that not accounting for the contribution of beneficial mutations to polymorphism data leads to substantially biased estimates of the DFE and alpha. We compare our framework with one of the most widely used inference methods available and apply it on a recently published chimpanzee exome data set

diCal-IBD: demography-aware inference of identity-by-descent tracts in unrelated individuals

UnlabelledWe present a tool, diCal-IBD, for detecting identity-by-descent (IBD) tracts between pairs of genomic sequences. Our method builds on a recent demographic inference method based on the coalescent with recombination, and is able to incorporate demographic information as a prior. Simulation study shows that diCal-IBD has significantly higher recall and precision than that of existing single-nucleotide polymorphism-based IBD detection methods, while retaining reasonable accuracy for IBD tracts as small as 0.1 cM.Availabilityhttp://sourceforge.net/projects/dical-ibd

Algorithms for Hidden Markov Models Restricted to Occurrences of Regular Expressions

Hidden Markov Models (HMMs) are widely used probabilistic models, particularly for annotating sequential data with an underlying hidden structure. Patterns in the annotation are often more relevant to study than the hidden structure itself. A typical HMM analysis consists of annotating the observed data using a decoding algorithm and analyzing the annotation to study patterns of interest. For example, given an HMM modeling genes in DNA sequences, the focus is on occurrences of genes in the annotation. In this paper, we define a pattern through a regular expression and present a restriction of three classical algorithms to take the number of occurrences of the pattern in the hidden sequence into account. We present a new algorithm to compute the distribution of the number of pattern occurrences, and we extend the two most widely used existing decoding algorithms to employ information from this distribution. We show experimentally that the expectation of the distribution of the number of pattern occurrences gives a highly accurate estimate, while the typical procedure can be biased in the sense that the identified number of pattern occurrences does not correspond to the true number. We furthermore show that using this distribution in the decoding algorithms improves the predictive power of the model

Regmex: a statistical tool for exploring motifs in ranked sequence lists from genomics experiments

Abstract Background Motif analysis methods have long been central for studying biological function of nucleotide sequences. Functional genomics experiments extend their potential. They typically generate sequence lists ranked by an experimentally acquired functional property such as gene expression or protein binding affinity. Current motif discovery tools suffer from limitations in searching large motif spaces, and thus more complex motifs may not be included. There is thus a need for motif analysis methods that are tailored for analyzing specific complex motifs motivated by biological questions and hypotheses rather than acting as a screen based motif finding tool. Methods We present Regmex (REGular expression Motif EXplorer), which offers several methods to identify overrepresented motifs in ranked lists of sequences. Regmex uses regular expressions to define motifs or families of motifs and embedded Markov models to calculate exact p-values for motif observations in sequences. Biases in motif distributions across ranked sequence lists are evaluated using random walks, Brownian bridges, or modified rank based statistics. A modular setup and fast analytic p value evaluations make Regmex applicable to diverse and potentially large-scale motif analysis problems. Results We demonstrate use cases of combined motifs on simulated data and on expression data from micro RNA transfection experiments. We confirm previously obtained results and demonstrate the usability of Regmex to test a specific hypothesis about the relative location of microRNA seed sites and U-rich motifs. We further compare the tool with an existing motif discovery tool and show increased sensitivity. Conclusions Regmex is a useful and flexible tool to analyze motif hypotheses that relates to large data sets in functional genomics. The method is available as an R package (https://github.com/muhligs/regmex)